Interpreting the Lancet surgical indicators in Somaliland: a cross-sectional study
Bibliographic record
Abstract
Background The unmet burden of surgical care is high in low-income and middle-income countries. The Lancet Commission on Global Surgery (LCoGS) proposed six indicators to guide the development of national plans for improving and monitoring access to essential surgical care. This study aimed to characterise the Somaliland surgical health system according to the LCoGS indicators and provide recommendations for next-step interventions. Methods In this cross-sectional nationwide study, the WHO’s Surgical Assessment Tool–Hospital Walkthrough and geographical mapping were used for data collection at 15 surgically capable hospitals. LCoGS indicators for preparedness was defined as access to timely surgery and specialist surgical workforce density (surgeons, anaesthesiologists and obstetricians/SAO), delivery was defined as surgical volume, and impact was defined as protection against impoverishment and catastrophic expenditure. Indicators were compared with the LCoGS goals and were stratified by region. Results The healthcare system in Somaliland does not meet any of the six LCoGS targets for preparedness, delivery or impact. We estimate that only 19% of the population has timely access to essential surgery, less than the LCoGS goal of 80% coverage. The number of specialist SAO providers is 0.8 per 100 000, compared with an LCoGS goal of 20 SAO per 100 000. Surgical volume is 368 procedures per 100 000 people, while the LCoGS goal is 5000 procedures per 100 000. Protection against impoverishing expenditures was only 18% and against catastrophic expenditures 1%, both far below the LCoGS goal of 100% protection. Conclusion We found several gaps in the surgical system in Somaliland using the LCoGS indicators and target goals. These metrics provide a broad view of current status and gaps in surgical care, and can be used as benchmarks of progress towards universal health coverage for the provision of safe, affordable, and timely surgical, obstetric and anaesthesia care in Somaliland.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".